Course Overview

In this training you will learn how Amazon EMR integrates with open-source projects such as Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.

Level: Intermediate

Duration: 1 Day

Delivery Type: Instructor-Led Training

Course Objectives

• Compare the features and benefits of data warehouses, data lakes, and modern data architectures
• Design and implement a batch data analytics solution
• Identify and apply appropriate techniques, including compression, to optimize data storage
• Select and deploy appropriate options to ingest, transform, and store data
• Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
• Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
• Secure data at rest and in transit
• Monitor analytics workloads to identify and remediate problems
• Apply cost management best practices

Prerequisites

Required

• Students with a minimum one-year experience managing open-source data frameworks such as Apache Spark or Apache Hadoop will benefit from this course.

Recommended

• AWS Technical Essentials
• Architecting on AWS
• Building Data Lakes on AWS

Who Should Go For This Training?

Data platform engineers Architects and operators who build and manage data analytics pipelines

Course Outline

Day 1

Module A: Overview of Data Analytics and the Data Pipeline

• Data analytics use cases
• Using the data pipeline for analytics

Module 1: Introduction to Amazon EMR

• Using Amazon EMR in analytics solutions
• Amazon EMR cluster architecture
• Interactive Demo 1: Launching an Amazon EMR cluster
• Cost management strategies

Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage

• Storage optimization with Amazon EMR
• Data ingestion techniques

Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR

• Apache Spark on Amazon EMR use cases
• Why Apache Spark on Amazon EMR
• Spark concepts
• Interactive Demo 2: Interactive analytics using Apache Spark on Amazon EMR
• Transformation, processing, and analytics
• Using notebooks with Amazon EMR
• Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR

Module 4: Processing and Analyzing Batch Data with Amazon EMR and Hive

• Using Amazon EMR with Hive to process batch data
• Transformation, processing, and analytics
• Practice Lab 2: Batch data processing using Amazon EMR with Hive
• Introduction to HBase on Amazon EMR

Module 5: Serverless Data Processing

• Serverless data processing, transformation, and analytics
• Using AWS Glue with Amazon EMR workloads
• Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions

Module 6: Security and Monitoring of Amazon EMR Clusters

• Securing EMR clusters
• Interactive Demo 3: Encrypting data at rest in Amazon EMR
• Monitoring and troubleshooting EMR clusters
• Demo: Reviewing Apache Spark cluster history
• Monitoring and troubleshooting Amazon EMR clusters

Module 7: Designing Batch Data Analytics Solutions

• Building Batch Data Analytics Solutions on AWS
• Batch data analytics use cases
• Activity: Designing a batch data analytics workflow

Module B: Developing Modern Data Architectures on AWS

• Modern data architectures